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This is the accepted version of this conference paper:

Clothier, Reece A., Palmer, Jennifer L., Walker, Rodney A., & Fulton, Neale L. (2010) Definition of airworthiness categories for civil Unmanned Aircraft Systems (UAS). In: 27th International Congress of the Aeronautical Sciences, 19‐24 September 2010, Acropolis Conference Centre, Nice. (In Press)

© Copyright 2010 Please consult the authors.

27TH

INTERNATIONAL CONGRESS OF THE AERONAUTICAL SCIENCES

1

Abstract

This paper introduces a novel strategy for the

specification of airworthiness certification

categories for civil unmanned aircraft systems

(UAS).

The risk-based approach acknowledges

the fundamental differences between the risk

paradigms of manned and unmanned aviation.

The proposed airworthiness certification matrix

provides a systematic and objective structure

for regulating the airworthiness of a diverse

range of UAS types and operations.

An approach for specifying UAS type

categories is then discussed. An example of the

approach, which includes the novel application

of data-clustering algorithms, is presented to

illustrate the discussion.

1 Introduction

The requirement for regulations governing the

airworthiness of a civil aircraft stems from the

Chicago Convention of 1944 [1]. Article 31 of

the Convention requires aircraft to be

certificated as airworthy, and Article 8 stipulates

the extension of these requirements to UAS. As

described in Annex 8 to the Convention, the

objective of these regulations is to achieve,

“among other things, protection of other

aircraft, third parties and property” [2].

Airworthiness, according to Australian

Defence Force (ADF) instructions [3], is: “…a

concept, the application of which defines the

condition of an aircraft and supplies the basis

for judgement of the suitability for flight of that

aircraft, in that it has been designed,

constructed, maintained and is expected to be

operated to approved standards and limitations,

by competent and approved individuals, who

are acting as members of an approved

organisation and whose work is both certified

as correct and accepted on behalf of the ADF.”

For civil conventionally piloted aviation

(CPA), airworthiness is assured through the

issuance of a Certification of Airworthiness

(CoA) to an individual aircraft or through

special certificates. A CoA is a formal statement

that the aircraft is verified as being compliant

with a prescriptive body of standards and

regulations that are defined based on its type.

The foundation of the CPA airworthiness

regulatory framework is established in Part 21

regulations [4, 5], which prescribe the

applicability of different codes of requirements

to the different types of aircraft and their

intended operation.

International consensus on a prescriptive

framework of airworthiness regulations for civil

unmanned aircraft systems (UAS) has yet to be

reached. Currently, there are no specific

standards and regulations for the type

certification of civil UAS.

In Australia, assurances of the safety of

other airspace users and people and property on

the ground are instead provided by the

placement of restrictions on where UAS

operations may take place. These restrictions are

mandated under Civil Aviation Safety

Regulations (CASR) Part 101 [6], which

specifies that a UAS must be certificated as

airworthy: if the unmanned aircraft1 (UA) has a

maximum takeoff weight (MTOW) above 150

1 The term unmanned aircraft refers only to the airborne

component of a UAS.

DEFINITION OF AIRWORTHINESS CATEGORIES FOR CIVIL UNMANNED AIRCRAFT SYSTEMS (UAS)

Reece A. Clothier,* Jennifer L. Palmer,

† Rodney A. Walker,

* Neale L. Fulton

‡

*Australian Research Centre for Aerospace Automation,

†Defence Science and Technology

Organisation, ‡Commonwealth Scientific and Industrial Research Organisation

Keywords: UAS, UAV, Airworthiness, Civil Regulations, Certification

REECE A. CLOTHIER, JENNIFER L. PALMER, RODNEY A. WALKER, NEALE L. FULTON

2

kg, if the UAS is to be operated over a

‘populated’ area or in controlled airspace, or for

commercial reward. UAS may be certificated in

the experimental designation (e.g., as described

in AC 21-43(0) [7]), for specific applications

and not for commercial reward, but remain

subject to operational restrictions. Operational

regulations such as CASR Part 101 [6] prescribe

the requirement for certification (primarily

based on the nature of the intended operation),

but not the specific type categories or categories

of airworthiness against which a CoA may be

issued.

The absence of a prescriptive airworthiness

certification framework and the subsequent

operational limitations imposed come at

significant expense to the UAS industry. A

prerequisite to the realisation of a viable civil

UAS industry is the definition of an appropriate

airworthiness certification framework for UAS.

This framework must take into consideration the

unique aspects of the technology, their

operations, the market drivers, and the broader

socio-political issues associated with the

integration of a new aviation technology into

society.

2 The Airworthiness Framework

The basis for an airworthiness framework for

civil UAS is established by acknowledging that

the primary entities of value (EoV) are external

to the UAS. The primary hazard of concern is

that of a UAS impacting a region on the ground

and the subsequent losses that could be

registered against different EoV in the region.

UAS may be operated over a diverse range of

areas, and hence the associated degree of risk

varies. Illustrations of the nature of this

dependency are provided in Refs. [8-10].

Conversely, for CPA, the primary risks are to

those people onboard the aircraft and hence

CPA airworthiness regulations are defined “as

far as is practicable” [11] independent of: the

nature of the operational environment and the

purpose for which the aircraft will be used in

service [11]. McGeer and Vagners [12] aptly

encapsulates the differences between the two

airworthiness paradigms:

…with a manned aircraft you have to build

to the same standard no matter what is

underneath you, but among unmanned

aircraft, acceptable safety for flights

exclusively over oceans can be achieved

with rather more rickety machines than

would be fit to fly over a city. [12]

A new concept for the structuring of

airworthiness regulations for civil UAS that

acknowledges this fundamental difference

between paradigms is required. One such

framework is proposed by Clothier et al. [13]

and illustrated in Fig.1. The framework is based

on the principles of a risk matrix and advocated

as being a suitable structure for the definition of

Part 21-equivalent regulations for civil UAS

[13]. The framework is briefly described in the

following sections.

2.1 Operational Environments

The set of m rows of the proposed

airworthiness certification matrix represent the

different environments that a UAS may be

operated over, ranked in order of increasing

‘susceptibility’ of an area to experience loss

given a UAS mishap (i.e., a categorisation of

operational areas ranging from the high seas

through to large open-air gatherings). These

categories are defined independent of the type

of UAS impacting the area. The set of

categories of operational environments must be

disjoint, and provide complete and contiguous

coverage of the range of operational

environments potentially over-flown (i.e.,

provide an unambiguous classification of all

potential areas over which a UAS operation

could occur).

2.2 Type Categories

The set of n columns of the matrix

represent the different type categories of UAS.

Each type category describes a grouping of

UAS where the magnitude of potential loss due

to a mishap is within some pre-defined bounds,

irrespective of where the UAS is operated. Or

more generally, UAS are grouped based on the

question: given the occurrence of an

unrecoverable flight critical failure, what is the


3

Fig. 1 Proposed structure of an airworthiness certification framework for civil UAS [13]

maximum degree of loss the UAS could cause,

irrespective of where it crashed?

The set of type categories must be disjoint

(i.e., provide an unambiguous classification of

the diverse range of UAS) and provide complete

and contiguous coverage of the range of

plausible magnitude of potential loss.

2.3 Operational Scenarios

Each of the m × n cells of the matrix

represents a unique operational scenario,

defined by the combination of a specific UAS

type category with a specific category of

operational environment. For each operational

scenario formed, an assessment of the level of

risk is then made.

2.4 Airworthiness Categories

Illustratively, the assignment of the r

airworthiness categories is the process of

assigning colours to each of the cells of the

matrix illustrated in Fig. 1.

The airworthiness categorisation scheme is

representative of a discrete, contiguous and

increasing ranking of risk (e.g., MIL-STD-882D

defines the risk-ranking scheme of low,

medium, serious, and high [14]). Each

operational scenario may then be mapped to one

of the airworthiness certification categories

based on the levels of risk assessed for the

operational scenario. Operational scenarios with

‘similar’ levels of risk may logically be assigned

to the same airworthiness certification category.

In general, the operational scenarios (and

subsequently the certification categories

assigned to them) in the lower-right quadrant of

the matrix present higher levels of risk than are

associated with the scenarios in the upper-left

quadrant.

Unlike the CPA airworthiness framework,

the aircraft-type category does not directly

define the airworthiness category. A UAS of a

specific type may be certificated in one or more

airworthiness categories dependent on the

category of operational area over which it is

intended to be flown. The approach permits

UAS manufacturers to develop and certify a

UAS-type for a specific application, operational

environment or price-point in the market.


4

A significant challenge is the determination

of an appropriate number of airworthiness

certification categories. The minimum number

of categories is one. This represents the

undesirable case where all UAS operational

scenarios are regulated under the same

standards irrespective of the differences in the

associated levels of risk.

The maximum number of airworthiness

categories is equal to the number of rows, m,

multiplied by the number of columns, n. This

represents the case where a separate body of

airworthiness regulations is defined for each of

the operational scenarios. This case offers the

maximum possible degree of tailoring of

regulations. However, Clothier et al. [13]

identify several disadvantages to a large number

of airworthiness categories. A determination of

the optimum number of airworthiness categories

requires subjective trade-offs to be made, for

example, determining the number of

airworthiness categories necessary to:

1. minimise the relative differences

between the levels of risk associated

with each operational scenario assigned

to a particular certification category (i.e.,

ensure operational scenarios assigned to

a given certification category are indeed

comparable in their risks);

2. ensure sufficient resolution in the

certification categories to permit niche or

unique operational scenarios; and

3. ensure a practical and workable

categorisation from the perspective of

the authority charged with promulgation

of the regulation (e.g., not unmanageable

in number).

Given an appropriate assignment of

certification categories to operational scenarios,

regulations may then be developed (or existing

CPA regulations tailored and adopted) to each

scenario. An example of this process, for civil

UAS Part 1309 regulations, is described in [13].

2.5 Summary

The airworthiness certification matrix

provides a flexible, yet systematic and

defensible framework for regulating the

airworthiness of civil UAS. The matrix provides

a suitable structure for simplifying the

classification of the diversity of systems and

operations. A systematic and transparent

process for assigning airworthiness categories is

described based on assessments of risk. This

assignment considers both the system and the

environment, hence the proposed airworthiness

certification matrix acknowledges the

fundamental differences between the CPA and

the UAS risk paradigms.

It is for these reasons, and others, that

Clothier et al. [13] advocate the proposed

airworthiness certification matrix as a suitable

basis for defining a Part 21-equivalent

regulation for civil UAS.

The remainder of this paper describes a

possible strategy for the specification of a

principal component of the proposed

airworthiness certification matrix, that of UAS

type categories (i.e., the columns of Fig. 1).

3 Specification of UAS Type Categories

For a comprehensive review of existing UAS

type categorisation schemes refer to the

forthcoming work of Nas [15].

Currently, there is no consensus on the

definition of type categories for UAS [16, 17],

with many of the existing schemes having not

been defined for the purpose of certification. In

addition, there is no consensus on the process

for defining a suitable scheme when such

consensus is fundamental to the progress of

regulations [16]. The specification of type

categories could have a significant influence on

the future ‘shape’ of the civil UAS industry,

hence a more objective and systematic process

for defining UAS type categories is needed. The

following sub-section describes one possible

approach that could be used to define type

categories for UAS.

3.1 A Risk-Based Approach

A type category is a grouping of UAS that

are similar in some relevant way. In accordance

with the structure of the proposed airworthiness

matrix (Fig. 1), the measure of similarity used to


5

define UAS type categories is the theoretical

magnitude of potential loss due to a mishap

(§2.2). A measurement of loss may be made for

a range of different types of EoV (e.g., people,

property, and environment) and entity attributes

(e.g., for people: physical, psychological, and

financial). Within the context of defining

airworthiness regulations for civil UAS, the

primary loss of concern is the magnitude of

physical harm to third parties on the ground.

Proceeding on this premise, a generalised two-

dimensional (2D) space describing the

magnitude of potential loss to people on the

ground is defined in Fig. 2. The two component-

dimensions are defined as:

X (or horizontal axis): the types of

potential harm to people exposed to a

mishap; and

Y (or vertical axis): the maximum number

of people that could be harmed given a

mishap.

Fig. 2 Illustration of the assignment of UAS type

categories within a 2D space describing the magnitude of

potential loss to people on the ground due to a UAS

mishap

The first component-dimension aims to

distinguish between UAS types based on their

ability to cause levels of physical harm to

people exposed to a UAS mishap. For example,

the physical and aerodynamic properties of

some UAS make them very unlikely to cause a

fatal injury to a person struck in the open. On

the other hand, some UAS are capable of

penetrating the strongest of structures and

consequently have the potential to cause harm to

the people sheltered within buildings.

A range of different hazards must be

considered to adequately characterise the ability

of a UAS to cause harm to people on the

ground, including:

1. primary hazards – e.g., the transfer of

energy through a direct strike, flying

debris, heat from an explosion, and

incident pressure waves; and

2. secondary hazards – e.g., the ensuing

collapse of a building, bush fires, and

on-going contamination by hazardous

substances.

To simplify this example, for this paper, the

UA is assumed to be in-frangible, and the UA

and any object that it strikes (e.g., a building or

vehicle) are inert (e.g., no secondary explosions

or collapsing walls are assumed possible);

however, a more comprehensive approach

would include a broader range of hazards.

Based on these assumptions, the primary hazard

may be considered as the transfer of kinetic

energy from the UA to people and structures on

the ground directly struck by the UA. The

measure used to characterise this component-

dimension is the maximum possible kinetic

energy of the UA on impact with the ground

(KEmax), given by:

2

maxMV2

1=maxKE (1)

where M is the MTOW of the UA and Vmax is its

maximum speed.

The second component-dimension aims to

distinguish amongst UAS types based on the

maximum number of potential casualties due to

mishap. Such a distinction is necessary to reflect

society’s heightened apprehension towards

mishaps with a large magnitude of potential

loss, irrespective of the associated likelihood of

occurrence [18].

Assuming independence of the particular

region over-flown (i.e., assuming an exposed

population of uniform distribution and of


6

uniform susceptibility or response to an incident

stress), the potential number of casualties is a

complex function of the spatial-temporal

distribution of the effects of a mishap. This

spatial-temporal region is referred to as the

hazard area.

Within the context of the simplified

example, the hazard area is the impact area of

the crashing UA. A range of models describing

the impact area of a crashing aircraft are

described in Refs. [19-24]. In this example, the

impact area, Iarea, is modelled as a simplified

glide area for a fixed-wing UA [21, 24], given

by:

,)2++(×)2+(= pglidepspanarea RDLRWI

(2)

where Wspan is the wingspan, Rp is the average

radius of a person (the shoulder width of an

average person), L is the length of the aircraft,

and Dglide is the distance along the ground

travelled by the UA at glide path angle, γ, from

the height of an average person, Hp, given by:

.tan γ

pglide

HD = (3)

3.1.1 Defining the Type Categories

The process of defining type categories is

one of determining mutually exclusive regions

within the 2D space where the magnitude of

potential loss due to a UAS mishap is similar

(Fig. 2). Two approaches for guiding the

specification of these regions are investigated in

this paper:

1. definition of limits with respect to one or

both of the component-dimensions (i.e.,

specification of loss criteria); and

2. application of a data-clustering

algorithm to ‘learn’ type categories from

a UAS dataset.

To ensure an unambiguous classification of

UAS types, the specification of type categories

must also satisfy:

PLoss(i) < PLoss(i + 1), for 1 ≤ i < r, (4)

where PLoss(i) is the potential magnitude of loss

associated with the ith

type category. Satisfying

this relationship requires subjective judgements

on the relative significance of the two

components used to represent the magnitude of

potential loss (i.e., the ranking of different loss

outcomes).

3.1.2 Categorisation Using Limits

The first approach to defining UAS type

categories is to specify boundaries with respect

to one or both axes of the 2D space describing

the magnitude of potential loss due to a UAS

mishap.

To illustrate, four high-level type

categories are defined based on the energy

required to cause a particular level of harm to a

person exposed to a UAS mishap (i.e., along the

impact-energy component-dimension). The type

categories and associated impact-energy

boundaries are summarised in Table 1. The first

category describes UAS that have sufficient

impact energy to cause injury (but not a fatal

injury) to one or more people exposed in the

open. A range of different injury scales could be

used to further describe this category (e.g., the

Abbreviated Injury Scale in Ref. [25]). The

second category describes UAS with impact

energy greater than 42 J but less than 1,356 J,

which are capable of causing fatal injuries to

one or more unsheltered people. The third

category describes UAS with sufficient impact

energy to penetrate a typical residential structure

(i.e., a corrugated-iron roof). The final category

describes UAS that are capable of penetrating

the highest level of protection afforded to the

general public (i.e., a reinforced concrete

structure).

A database2 comprised of over 500 rotary

and fixed-wing UAS types is used to illustrate

the outcome type categories. Three-hundred and

eighty-three fixed-wing UAS types are mapped

to the 2D space by use of Eqs. 1–3. These UA

are then colour coded based on the type-

classification scheme described above. Fig. 3

shows the classification of the fixed-wing UAS

dataset based on the categories defined in Table

1. Fig. 4 shows the same classification mapped

2 Database of UAS compiled and maintained by Defence

Science and Technology Organisation (DSTO) personnel.

Database includes military UAS.


7

Category Description of loss outcome Energy Limit* (J) Description of Energy Limit

1 UAS capable of causing a non-fatal

injury to one or more exposed

people

KEmax < 42

< 5% probability of causing a fatal injury

to an individual standing in the open [26]

2 UAS capable of causing a fatal

injury to one or more exposed

people

42 ≤ KEmax < 1,356

≥ 5% probability of causing a fatal injury

to an individual standing in the open [26]

3 UAS capable of causing a fatal†

injury to one or more people within

a typical residential structure

1,356 ≤ KEmax < 13,560

Capable of penetrating a corrugated-iron

roof house [27]

4 UAS capable of causing a fatal†

injury to one or more people within

a typical commercial structure

KEmax ≥ 13,560

Capable of penetrating a reinforced

concrete structure [27]

*Energy limits are based on the conservative assumption that each UA may be represented as an in-frangible piece of

inert debris. †It is assumed that any individual inside a structure is fatally injured if the UA has sufficient energy to penetrate the

structure.

Table 1 Type categories based on the ability of the UAS to cause harm

Fig. 3 Type categorisation of fixed-wing UAS based on

impact energy limits

Fig. 4 Type categorisation displayed using UA MTOW

against the MTOW of the UA, a common metric

used in existing type categorisation schemes.

The mapping of the categories with respect

to the MTOW of each UA type, shown in Fig. 4,

results in an ambiguous classification. This

highlights that a measure of the MTOW of a

UA, on its own, may not provide a good

discriminator with respect to the potential harm

it may cause. One must also consider the

maximum potential speed of the UA upon

impact. From Fig. 3 and 4, it may be observed

most UAS of MTOW greater than 200 g are

capable of inflicting a fatal injury to one or

more people exposed in the open. In addition,

nearly all UAS with a MTOW greater than ~20

kg have sufficient maximum impact energy to

penetrate the highest level of protection

typically available to a member of the general

public. Consequently, type category 4 covers a

disproportionately large range of UAS types.

Greater resolution within the type categories

may be required. This may be obtained by

defining additional categories of harm along the

impact-energy axis (e.g., types of sheltering,

levels of injury, or a combination of both) or by

defining limits with respect to the second

component-dimension (i.e., the impact area).

Classifying with respect to the maximum

impact area of the UA attempts to distinguish

UAS type categories based on the number of


8

people potentially harmed. The larger the

impact area, the more people potentially

exposed to harm given a UAS mishap. Relating

the impact area to a number of potential

casualties (i.e., through the specification of a

population distribution) and the specification of

category boundaries in terms of a number of

casualties is a subjective process. A more

objective approach is to use a data-clustering

algorithm to objectively ‘learn’ type categories

from the UAS dataset.

3.1.3 Categorisation by Use of Data Clustering

Data clustering or data segmentation [28]

is a process of unsupervised learning [29] that

attempts to organise a collection of objects into

a finite number of meaningful groupings, or

clusters, based on some measure of similarity

[30]. In this example, the objects are individual

UAS types and the measure of similarity is the

impact area of the UA, given by Eq. 2.

Data-clustering techniques have been

widely applied, particularly in the field of image

processing, with clustering algorithms being

used for image compression, segmentation, and

pattern recognition for machine vision. It is not

the objective of this paper to provide a thorough

overview of data-clustering techniques. For this,

the reader is referred to the introductory paper

of Jain and Dubes [31]. Instead, the aim here is

to emphasise that an alternative and more

objective approach to the definition of UAS

type-certification categories may lie in the

application of data clustering techniques.

In this example, the widely applied [32] k-

means algorithm [33] is used to determine sub-

type categories within the fourth type category

illustrated in Fig. 3. The k-means algorithm is a

partitioning relocation algorithm [32] that

determines a finite number of k clusters/groups

directly from the data by iteratively relocating

points until a minimum in an objective distance

measure is obtained. k is an integer value less

than or equal to the number of unique UAS

types in the dataset (K).

The k-means algorithm has the advantages

of being extensively used, simple to interpret

and implement, and computationally efficient.

One of the significant disadvantages is that the

convergence of the algorithm is highly

dependent on its initialisation state [28-30].

Dependent on the initial conditions, the

algorithm may converge to a local optimum [28]

and consequently may not find a globally

optimal partitioning of UAS types into

categories. Another significant disadvantage of

the algorithm is that the number of clusters (k)

must be known a priori.

To address these disadvantages, the k-

means algorithm was run for values of k ranging

from 2 to 7. The algorithm was run fifty times

for each value of k, with randomly selected

initial conditions. The solution with the lowest

sum of intra-cluster distances was then selected

from the set of fifty possible solutions. As

advocated by Kaufman and Rousseeuw [32],

the silhouette coefficient (SC) may be used to

objectively compare the performance of the

clustering algorithm for each value of k. SC is

the maximum of the average silhouette width

for the entire dataset, a dimensionless measure

of how well each object within a dataset has

been classified [32] (refer to p.83 of Ref. [32]

for a description of its calculation). SC ranges

between –1 and +1, with the values of –1, 0 and

+1 indicative of a poor, an indifferent, and an

appropriate classification of UAS into type

categories, respectively. SC for each value of k

is presented in Table 2.

No. of type

sub-

categories,

k

Maximum

average SC

Qualitative assessment* of

how well the classification

represents the structure of

the dataset

2 0.822 strong

3 0.705 strong

4 0.706 strong

5 0.692 reasonable

6 0.710 strong

7 0.720 strong *Based on the qualitative assessment table presented in

Kaufman and Rousseeuw [32].

Table 2 Results obtained from clustering approach for

different numbers of type categories

Based on the results presented in Table 2,

the mathematically optimal solution is to sub-

divide the fourth UAS type category into two

sub-categories (k = 2, Table 2). This results in a

total of five UAS type categories.

DEFINITION OF AIRWORTHINESS CATEGORIES FOR UNMANNED AIRCRAFT SYSTEMS (UAS)

9

Fig. 5 Classification of UAS into five type categories

Type

category

Boundary conditions Example UAS

1 KEmax < 42 J Black Widow,

Hornet

2 42 J ≤ KEmax < 1,356 J Pointer, Raven

3 1,356 J ≤ KEmax < 13,560 J ScanEagle,

Aerosonde Mk4

4 13,560 J ≤ KEmax

Iarea < 347 m2

Shadow 600

5 347 m2 ≤ Iarea

Heron 1,

Taranis, Global

Hawk

Table 3 Example output UAS type categorisation scheme

A visualisation of the candidate five-category

UAS type categorisation scheme is illustrated in

Fig. 5; and Table 3 summarises the boundaries

of the example type categorisation scheme.

It may be observed in Table 2 that the SC

determined for each value of k varies little, and

hence the ideal number of type categories is

more likely to be determined by the context of

the problem (i.e., broader subjective trade-offs,

as described in §2.4), rather than by the

mathematically optimal solution. Thus, the

results presented in Fig. 5 and Table 3 are

illustrative only.

3.4 Discussion

The assumptions and simplified model

used in the previous example place limitations

on the usability of the results; however, the

intention here is to illustrate an objective

approach for specifying UAS type categories.

The basis for classification is the magnitude of

potential loss a UAS may cause given a mishap,


10

independent of where it is flown. Two

techniques for specifying the boundaries of the

type categories have been described. However,

it is unlikely that this process will ever be

reduced to a purely mathematical approach. The

specification of type categories will heavily

influence the nature of the future

civil/commercial UAS industry; hence, it is

likely the decision-making process will involve

multiple, competing stakeholders and be a

predominantly discursive process. The

approaches presented in this paper provide a

focus for a risk-informed basis to help guide this

higher-order decision-making process.

4 Summary and Conclusions

This paper summarises a novel strategy for the

specification of airworthiness certification

categories for civil UAS. The proposed

framework, outlined in §2, comprises two

orthogonal dimensions (representative of the

potential magnitude of loss and the potential for

realising loss, given a UAS impacting the

ground). The Cartesian product of the two

dimensions defines a finite set of operational

scenarios, to which airworthiness categories are

then assigned based on an assessment of the

risks. In §3, the specification of the columns of

the matrix (a type-categorisation scheme for

UAS) was discussed and presented in simplified

form. As discussed in §2.4, an appropriate

system of certification categories for the

purpose of regulating civil UAS may be based

on such a risk matrix.

The risk matrix approach permits a

tailoring of airworthiness regulation in line with

the risks. For example, small UAS, which

present lower levels of risk, are subject to lower

standards of airworthiness and hence may be

permitted a higher mishap rate. Larger UAS,

which present risks comparable to CPA, attract

a level of airworthiness regulation comparable

to CPA.

The airworthiness matrix approach

provides a systematic and justifiable (through

traceability in risk) framework for the regulation

of the diverse range of UAS and their

operations. The matrix affords a greater degree

of flexibility in the regulation of UAS allowing

UAS manufacturers to develop and certify a

UAS-type for a specific application, operational

environment or price-point in the market.

Broader social, political, technical, and

practical considerations will ultimately

determine the refinement of any airworthiness

certification scheme adopted for civil UAS;

however, the approach presented in this paper

provides the risk-informed foundations from

which to guide these discussions. The

comprehensive specification of the

airworthiness certification matrix, including the

specification of UAS type categories and

operational environments, will be the subject of

a future paper.

5 Contact Author Email Address

The corresponding author for this paper is Mr.

Reece Clothier: [email protected].

Acknowledgement

The authors would like to thank Mr. Michael

Nas, from Murdoch University, Perth, Australia,

and members from the Australian Aerospace

Industry Forum, Certification and Regulation

Working Group, Unmanned Aircraft System

Sub-Committee for their input to this paper. The

authors would also like to thank Dr XunGuo Lin

for his comments made in reviewing this paper.

This research is supported by a Queensland

State Government Smart State PhD Scholarship.

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